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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The image that runs Interlisp-D and Medley on Linux seems to actually be a Workbench for Lexical Functional Grammar. I can only guess that this is a customisation of the Medley programming environment ...
- Install or download VirtualBox
These notes are based upon the steps provided here:
The notes in this Gist were created due to multiple attempts at installing Open Genera on different platforms that required more steps not inlcuded in the source notes above.
Most of this was taken from the project instructions here:
Other materials referecned:
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |


